About me

Welcome to the online academic space of Lida Kanari! I am a Lecturer of Applied Mathematics at the University of Oxford. I lead the Computational Algebraic Topology for Neuroscience group, which is dedicated to exploring how topology and applied mathematics can shape the future of neuroscience. Combining Topological Data Analysis, Machine Learning and Camputational Modeling, we study the properties of brain morphologies with the objective to understand pathologies and improve diagnostic tools.

Biography

I am a mathematician with a passion for understanding biological processes. After completing my undergraduate studies in mathematics and physics at the National Technical University of Athens, I pursued a PhD in computational neuroscience (2012-2018) at the Blue Brain Project in EPFL, Lausanne. During my doctoral research, I made significant contributions to understanding the shapes of neurons (Kanari et al. 2018, Kanari et al. 2019, Zisis et al. 2021, Colombo at al. 2023), using algebraic topology, by representing neurons and glia cells with topological barcodes. My innovative approach opened new avenues for analyzing complex branching structures such as neurons, blood vessels, and botanic trees. In addition, I developed techniques to computrationally generate dendritic morphologies based on their topological properties (Kanari et al. 2022).

After my PhD, I became the group leader of the NeuroMathematics team (2019-2024) at the Blue Brain Project. Leading a research team of 8 postdoctoral researchers and engineers, we extended my previous work to study the differences between species (Kanari et al. 2025), computationally synthesize axonal morphologies across brain regions (Berchet et al. 2025, Petkantchin et al. 2025) and study the links between morphologies and other modalities (Arnaudon et al. 2024, Veraszto et al. 2024). My research interests encompass a wide range of topics, including algebraic topology, stochastic processes, branching trees, and graphs. Through my interdisciplinary work, I strive to unravel the underlying mathematical principles governing the brain’s intricate organization and function.

I joined the University of Oxford in 2025, as a Research Fellow with an MRC, UKRI grant to start my group “Algebraic Topology with Applications in Computational Neuroscience.” I am excited for this new opportunity and I look forward to building an interdisciplinary team that will explore how algebraic topology can bridge the gap between single-neuron morphology and large-scale brain networks. By combining rigorous mathematics with modern computational methods, my group aims to develop new frameworks for analyzing, classifying, and generating neuronal structures, while also uncovering fundamental principles of brain connectivity.

Beyond neuroscience, I am passionate about advancing the field of applied topology and its role in biological sciences more broadly. I envision that the methods we develop will not only deepen our understanding of brain structure and function, but also provide tools applicable to diverse branching systems in nature and technology. Through this work, I strive to contribute to a more unified mathematical language for complex biological forms.

Selected Publications

1. Petkantchin, R., Berchet, A., Peng, H. et al. Generating brain-wide connectome using synthetic axonal morphologies. Nat Commun 16, 6611 (2025). doi.org/10.1038/s41467-025-62030-3
2. Kanari L, Shi Y, Arnaudon A, et al. Of mice and men: Dendritic architecture differentiates human from mouse neuronal networks. iScience. 2025;28(7):112928. Published 2025 Jun 18. doi:10.1016/j.isci.2025.112928
3. Colombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L. H., Chachólski, W., Hess, K., & Siegert, S. (2022). A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Nature neuroscience, 25(10), 1379–1393. doi.org/10.1038/s41593-022-01167-6
4. Kanari L., Dictus H., Chalimourda A., van Geit W., Coste B, Shillcock J., Hess K., and Markram H. (2020). Computational synthesis of cortical dendritic morphologies. Cell Reports. doi.org/10.1101/2020.04.15.040410.
5. Kanari, L., Dlotko, P., Scolamiero, M., Levi, R., Shillcock, J.C., Hess, K., and Markram, H. (2017). A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics, 16, 3 - 13. doi.org/10.1007/s12021-017-9341-1

Selected Preprints

1. Michael W. Reimann, Daniela Egas Santander, Lida Kanari and Natali Barros-Zulaica (2025). Neuron morphological physicality and variability define the non-random structure of connectivity. bioRxiv.
2. Kanari, L., Schmidt, S., Casalegno, F., Delattre, É., Banjac, J., Negrello, T., Shi, Y., Meystre, J., Defferrard, M., Schürmann, F., and Markram, H. (2024). Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks. bioRxiv.
3. Csaba Verasztó, Yann Roussel, Lida Kanari, Henry Markram, Daniel Keller (2024). A quantitative spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain. bioRxiv.

Funding

• 2024 – UKRI / MRC Research Grant (£1.4M)
  Algebraic topology bridging the gap between single neurons and networks

• 2024 – Career Development Grant ($100K)
  Role of top-down contextual representations and inhibitory sub-populations in neocortical mismatch responses
  (co-CP with Eilif Muller, Catalyseur – Fondation Courtois)

• 2023 – SAGES Grant ($14K)
  S1-persistence modules and Persistent Homology Transform
  (co-PI with Kate Turner and Barbara Giunti)

• 2023 – ICERM Travel Grant ($3K)
  Mathematical Challenges

• 2022 – Workshop Organization Grant (30K CHF)
  Mouse Neuronal Morphologies in Health and Disease: Reconstruction, Analysis and Modelling

Patents

Title : Synthesis of branching morphologies

Application No : 20190100470

Inventors : Lida Kanari, Kathryn Hess Bellwald, Henry Markram

For more info